Sigma Jahan

Sigma Jahan, Saurabh Singh Rajput, Tushar Sharma et al.

Transformer models are widely deployed in critical AI applications, yet faults in their attention mechanisms, projections, and other internal components often degrade behavior silently without raising runtime errors. Existing fault diagnosis techniques often target generic deep neural networks and cannot identify which transformer component is responsible for an observed symptom. In this article, we present DEFault++, a hierarchical learning-based diagnostic technique that operates at three level of abstraction: it detects whether a fault is present, classifies it into one of 12 transformer-specific fault categories (covering both attention-internal mechanisms and surrounding architectural components), and identifies the underlying root cause from up to 45 mechanisms. To facilitate both training and evaluation, we construct DEFault-bench, a benchmark of 3,739 labeled instances obtained through systematic mutation testing. These instances are created across seven transformer models and nine downstream tasks using DEForm, a transformer-specific mutation technique we developed for this purpose. DEFault++ measures runtime behavior at the level of individual transformer components. It organizes these measurements through a Fault Propagation Graph (FPG) derived from the transformer architecture. It then produces an interpretable diagnosis using prototype matching combined with supervised contrastive learning. On DEFault-bench, DEFault++ exceeds an AUROC of 0.96 for detection and a Macro-F1 of 0.85 for both categorization and root-cause diagnosis on encoder and decoder architectures. In a developer study with 21 practitioners, the accuracy of choosing correct repair actions increased from 57.1% without support to 83.3% when using DEFault++.

Sigma Jahan

Deep Learning (DL) programs can fail during training for many reasons, and diagnosing the cause is a costly and time-consuming maintenance task. Techniques for diagnosing such failures are commonly assessed using within-program cross-validation, which may be inadequate for deployment settings involving previously unseen programs. It is therefore necessary to assess how performance differs across these settings and to identify the causes of any performance gap in established fault diagnosis techniques for DL. We investigate this gap using DynFault, a corpus of 5,542 fault-injected training traces from 38 real-world DL programs. We found a gap of 0.190 in balanced accuracy for existing fault diagnosis techniques between within-program evaluation and holding out whole programs. We also found the gap comes from program-level structure in the features, which led us to examine two runtime feature sets, curvature features and optimizer features, and their behavior on unseen programs. We found that curvature features are useful for instability detection on unseen programs, while optimizer and activation features help only on programs seen during training.

Sigma Jahan, Saurabh Singh Rajput, Tushar Sharma et al.

Attention mechanisms are at the core of modern neural architectures, powering systems ranging from ChatGPT to autonomous vehicles and driving a major economic impact. However, high-profile failures, such as ChatGPT's nonsensical outputs or Google's suspension of Gemini's image generation due to attention weight errors, highlight a critical gap: existing deep learning fault taxonomies might not adequately capture the unique failures introduced by attention mechanisms. This gap leaves practitioners without actionable diagnostic guidance. To address this gap, we present the first comprehensive empirical study of faults in attention-based neural networks (ABNNs). Our work is based on a systematic analysis of 555 real-world faults collected from 96 projects across ten frameworks, including GitHub, Hugging Face, and Stack Overflow. Through our analysis, we develop a novel taxonomy comprising seven attention-specific fault categories, not captured by existing work. Our results show that over half of the ABNN faults arise from mechanisms unique to attention architectures. We further analyze the root causes and manifestations of these faults through various symptoms. Finally, by analyzing symptom-root cause associations, we identify four evidence-based diagnostic heuristics that explain 33.0% of attention-specific faults, offering the first systematic diagnostic guidance for attention-based models.

Sigma Jahan, Mohammad Masudur Rahman

As attention-based deep learning models scale in size and complexity, diagnosing their faults becomes increasingly challenging. In this work, we conduct an empirical study to evaluate the potential of Hessian-based analysis for diagnosing faults in attention-based models. Specifically, we use Hessian-derived insights to identify fragile regions (via curvature analysis) and parameter interdependencies (via parameter interaction analysis) within attention mechanisms. Through experiments on three diverse models (HAN, 3D-CNN, DistilBERT), we show that Hessian-based metrics can localize instability and pinpoint fault sources more effectively than gradients alone. Our empirical findings suggest that these metrics could significantly improve fault diagnosis in complex neural architectures, potentially improving software debugging practices.